Reversal learning data

Reversal learning data

Our reversal learning data contains data about a fairly large number of subjects.

Here you can see their behavior over time.

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

main.prop.cor.ggplot<-
  ggplot(accuracy.by.pres_seg.subid[!is.na(presentation_n_over_segments)],
       aes(x=presentation_n_over_segments,y=prop.correct,group=subid))+
  geom_line(aes(colour=final.prop.correct),size=1.5,alpha=0.3)+ scale_colour_gradientn(colours=c("red","green","blue","violet"))+
  #scale_x_continuous(breaks=-8:4,labels=break.labels)+
  labs(#x="Presentation",
    y="Proportion correct across all images by user",
    title=paste0("proportion correct across all images by user\n from start to finish of reversal learning"))+
  geom_smooth(group=1,color="black",span=1)+
  #geom_line(data=accuracy.by.pres_seg.subid.summary,aes(x=presentation_n_over_segments,y=prop.correct.m,group=NULL))+
      facet_grid(Motivation ~ .)+
  geom_vline(aes(xintercept=8),linetype=2)+geom_vline(aes(xintercept=13),linetype=2)+
  #theme(strip.text.y=element_text(colour="orange"))+
  reversal_learning_timeline_ggplot_commands+
  geom_hline(data=accuracy.by.pres_seg.subid.summary[presentation_n_over_segments==5],
             aes(yintercept = prop.correct.m),
             linetype=2)+
  geom_hline(data=accuracy.by.pres_seg.subid.summary[presentation_n_over_segments==13],
             aes(yintercept = prop.correct.m),
             linetype=2)

ggplotly(main.prop.cor.ggplot)

## Warning: We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

## `geom_smooth()` using method = 'gam'

source("shiny_graph_backend.R")